Figure 1: Given a set of cluttered 3D point clouds of building interiors (LEFT), we use fitting rectangles of the planar components as simplified scene description, and separate those belonging to permanent structures (green) from clutter (red) (MIDDLE). From the dominant planes of the permanent components we build a 3D cell complex, whose cells are partitioned to create individual room polyhedra (RIGHT). AbstractReconstructing the as-built architectural shape of building interiors has emerged in recent years as an important and challenging research problem. An effective approach must be able to faithfully capture the architectural structures and separate permanent components from clutter (e.g. furniture), while at the same time dealing with defects in the input data. For many applications, higher-level information on the environment is also required, in particular the shape of individual rooms. To solve this ill-posed problem, state-of-the-art methods assume constrained input environments with a 2.5D or, more restrictively, a Manhattan-world structure, which significantly restricts their applicability in real-world settings. We present a novel pipeline that allows to reconstruct general 3D interior architectures, significantly increasing the range of real-world architectures that can be reconstructed and labeled by any interior reconstruction method to date. Our method finds candidate permanent components by reasoning on a graph-based scene representation, then uses them to build a 3D linear cell complex that is partitioned into separate rooms through a multi-label energy minimization formulation. We demonstrate the effectiveness of our method by applying it to a variety of real-world and synthetic datasets and by comparing it to more specialized state-of-the-art approaches.

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We present a method to automatically segment indoor scenes by detecting repeated objects. Our algorithm scales to datasets with 198 million points and does not require any training data. We propose a trivially parallelizable preprocessing step, which compresses a point cloud into a collection of nearly-planar patches related by geometric transformations. This representation enables us to robustly filter out noise and greatly reduces the computational cost and memory requirements of our method, enabling execution at interactive rates. We propose a patch similarity measure based on shape descriptors and spatial configurations of neighboring patches. The patches are clustered in a Euclidean embedding space based on the similarity matrix to yield the segmentation of the input point cloud. The generated segmentation can be used to compress the raw point cloud, create an object database, and increase the clarity of the point cloud visualization.

Creating high‐level structured 3D models of real‐world indoor scenes from captured data is a fundamental task which has important applications in many fields. Given the complexity and variability of interior environments and the need to cope with noisy and partial captured data, many open research problems remain, despite the substantial progress made in the past decade. In this survey, we provide an up‐to‐date integrative view of the field, bridging complementary views coming from computer graphics and computer vision. After providing a characterization of input sources, we define the structure of output models and the priors exploited to bridge the gap between imperfect sources and desired output. We then identify and discuss the main components of a structured reconstruction pipeline, and review how they are combined in scalable solutions working at the building level. We finally point out relevant research issues and analyze research trends.

Abstract-We present a robust approach for reconstructing the architectural structure of complex indoor environments given a set of cluttered input scans. Our method first uses an efficient occlusion-aware process to extract planar patches as candidate walls, separating them from clutter and coping with missing data. Using a diffusion process to further increase its robustness, our algorithm is able to reconstruct a clean architectural model from the candidate walls. To our knowledge, this is the first indoor reconstruction method which goes beyond a binary classification and automatically recognizes different rooms as separate components. We demonstrate the validity of our approach by testing it on both synthetic models and real-world 3D scans of indoor environments.